MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification

被引:29
作者
Chhabria, Vidya A. [1 ]
Zhang, Yanqing [2 ]
Ren, Haoxing [2 ]
Keller, Ben [2 ]
Khailany, Brucek [2 ]
Sapatnekar, Sachin S. [1 ]
机构
[1] Univ Minnesota, Minneapolis, MN 55455 USA
[2] NVIDIA Corp, Santa Clara, CA USA
来源
PROCEEDINGS OF THE 2021 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2021) | 2021年
关键词
D O I
10.23919/DATE51398.2021.9473914
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Vectored IR drop analysis is a critical step in chip signoff that checks the power integrity of an on-chip power delivery network. Due to the prohibitive runtimes of dynamic IR drop analysis, the large number of test patterns must be whittled down to a small subset of worst-case IR vectors. Unlike the traditional slow heuristic method that select a few vectors with incomplete coverage, MAVIREC uses machine learning techniques-3D convolutions and regression-like layers-for accurately recommending a larger subset of test patterns that exercise worst-case scenarios. In under 30 minutes, MAVIREC profiles 100K-cycle vectors and provides better coverage than a state-of-the-art industrial flow. Further, MAVIREC's IR drop predictor shows 10X speedup with under 4mV RMSE relative to an industrial flow.
引用
收藏
页码:1825 / 1828
页数:4
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